Abstract: Animals and birds entering restricted human zones need to be quickly and accurately detected to avoid crop damage, traffic accidents, and conflicts between humans and wildlife. This proposed work describes the development of a useful AI-driven real-time Animals and Birds Detection and Alert System on a custom dataset. The proposed pipeline detects various species of animals and birds present in images, videos, and live streaming feeds. Upon detection of hazards, it automatically triggers user-configurable alerts such as SMS notifications, buzzer/siren sounds, etc. We present the model architecture, training, deployment plan, and performance evaluation on diverse scenes, such as highways and farms. The system allows for live feeds from IP cameras, user uploads, incident review dashboards, and alert logging for future monitoring and analysis. A scalable, modular design allows for future system expansion, integration with IoT components, and effective real-time inference suitable for edge deployment in rural and forest border areas.

Keywords: human-wildlife conflict prevention, bird detection, YOLO, real-time surveillance, Flask, alert system, deep learning, edge AI.

Downloads: | DOI: 10.17148/IARJSET.2025.1211046